Role of Noncanonical Wnt Signaling Pathway in Human Aortic Valve Calcification
Bibliographic record
Abstract
Objective— The mechanisms underlying the pathogenesis of aortic valve calcification remain unclear. With accumulating evidence demonstrating that valve calcification recapitulates bone development, the crucial roles of noncanonical Wnt ligands WNT5a, WNT5b, and WNT11 in osteogenesis make them critical targets in the study of aortic valve calcification. Approach and Results— Using immunohistochemistry, real-time qPCR, Western blotting, and tissue culture, we examined the tissue distribution of WNT5a, WNT5b, and WNT11 in noncalcified and calcified aortic valves and their effects on human aortic valve interstitial cells (HAVICs). Only focal strong immunostaining for WNT5a was seen in and around areas of calcification. Abundant immunostaining for WNT5b and WNT11 was seen in inflammatory cells, fibrosis, and activated myofibroblasts in areas of calcified foci. There was significant correlation between WNT5b and WNT11 overall staining and presence of calcification, lipid score, fibrosis, and microvessels ( P <0.05). Real-time qPCR and Western blotting revealed abundant expression of both Wnts in stenotic aortic valves, particularly in bicuspid valves. Incubation of HAVICs from noncalcified valves with the 3 noncanonical Wnts significantly increased cell apoptosis and calcification ( P <0.05). Treatment of HAVICs with the mitogen-activated protein kinase-38β and GSK3β inhibitors significantly reduced their mineralization ( P <0.01). Raman spectroscopy identified the inorganic phosphate deposits as hydroxyapatite and showed a significant increase in hydroxyapatite deposition in HAVICs in response to WNT5a and WNT11 ( P <0.05). Similar crystallinity was seen in the deposits found in HAVICs treated with Wnts and in calcified human aortic valves. Conclusions— These findings suggest a potential role for noncanonical Wnt signaling in the pathogenesis of aortic valve calcification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".